Monopolizing Knowledge, Part 3: Clarity

In his new book Monopolizing Knowledge (available for purchase now), physicist Ian Hutchinson engages with the world-view he calls “scientism”: “the belief that science, modeled on the natural sciences, is the only source of real knowledge” (page vii). In Hutchinson’s eyes, this erroneous world-view is at least indirectly responsible for the apparent friction between science and religion that many see today. In this series (taken from the larger book, which engages the topic in a much fuller and deeper fashion), Hutchinson will attempt to both explain and dismantle “scientism” by examining both what we mean when we say “science”, and how the scientistic worldview oversteps this definition and becomes a philosophical and metaphysical framework. In part 1, we took a brief look at the origins of scientism. In Part 2 , Hutchinson described "reproducibility", the first of two key characteristics of science that underlie its immense power but limit its scope. Today, we move on to explore the second key characteristics of true science: Clarity.

The second major characteristic that natural science requires I refer to as `Clarity'. I use capitalization to indicate that the word is being used in a specialized sense. Clarity is a requirement for the expression and communication of reproducibility; so these two scientific traits are partners. The results of any scientific investigation have to be expressed in terms that are unambiguous. Otherwise it is not possible for other investigators, or indeed even the same investigator, to tell whether repeating the experiment or observation gives the same answer as on the prior occasion.

The most direct way to ensure this unambiguous Clarity is for scientific observations to consist of measurements. The consequent reduction of the main parameters of the observations to numbers offers Clarity because numbers are probably the most familiar universal concept of thought. Measurement is more than numbers though. To measure the physical world we need shared scales, references, and common systems of units, which are crucial supporting parts of science.

Figure 1: The replica K48 of the official kilogram, kept by the Danish National Metrology Institute under two bell-jars.1

Clarity is a deliberately vaguer description of the crucial characteristic of science expression than mathematics. That is for a good reason. The description of experiment and of field observations involves a lot more than what most people would recognize as mathematics. And yet those aspects of science also strive for Clarity and precision. The ability to focus on the important aspects of the phenomena, and avoid over-burdening descriptions with endless details that are irrelevant, is something that is learned by scientists through apprenticeship and numerous informal experiences, not formal courses. So, paradoxically perhaps, scientific Clarity is something that can't be learnt or prescribed with Clarity.

There are many important questions that inherently lack the kind of Clarity that science requires. Consider the beauty of a sunset, the justice of a verdict, the compassion of a nurse, the drama of a play, the depth of a poem, the terror of a war, the excitement of a symphony, the significance of a history, the love of a woman. Which of these can be reduced to the Clarity of a scientific description? Yes, a sunset can be described in terms of the spectral analysis of the light, the causes of the coloration arising from light scattering by particles and molecules, and their arrangement and gradient in the sky. But when all the scientific details of such a description are done, has that explained, or even conveyed, its beauty? Hardly. In fact it has missed the point. Many-layered connections and implications are intrinsically part of the significance of these subjects. We appreciate and understand them, we know them, through sharing conceptually in the interwoven fabric of their often only evocative allusions. Removal of ambiguity destroys that significance, because ambiguity is at the very heart of their meaning. One cannot appreciate ambiguity unambiguously. Consequently, matters such as these cannot be encompassed scientifically.

This is not a problem for science. But under scientism's presumptions there's a major problem for subjects like these. They either have to be dismissed from the set of topics offering any real knowledge or they have to be reduced to something like science. The first strategy is often at work, for example, when people talk about the distinction between fact and value. If scientism places the discussion of value into a category of non-knowledge, of unsupported doctrine, of mere opinion, it fundamentally undermines its significance. The second strategy: reducing the topic to something like science by imposing on it the structures of scientific analysis, is commonplace in many social disciplines, and spills over, for example, into popular journalism through an excessive reliance on opinion polls.

Reduction or reductionism is sometimes regarded as a principle of science. A complex system is explained in terms of the interaction of a number of simpler subsystems from which it is composed. Those subsystems may themselves be further explained by a lower hierarchy of sub-subsystems, and so on. Actually, though, this general strategy has a much simpler name than reductionism. It is precisely what is meant by analysis: the separating of something into its constituent parts in order to understand it. A scientistic viewpoint very often adopts reductionism not just as a useful method, but as an inviolable principle. It is presumed that when a satisfactory scientific explanation at a reduced level exists the higher level descriptions lose their force or relevance. Donald MacKay2 coined the disparaging but descriptive phrase "nothing-buttery" to refer to this ontological reductionism. "Nothing-buttery is characterized by the notion that by reducing any phenomenon to its components you not only explain it, but explain it away". It is definitely helpful to analyze animal bodies in terms of their cells, but it is unhelpful, and fundamentally untrue, to conclude that if one completes such an analysis, then animals are demonstrated to be nothing but assemblies of cells.

It is more helpful, instead of focusing on reductionist explanation in science, to think in terms of the integration of new phenomena, specimens, or models into the overall network of scientific description. An analogy that seeks to express the cross-connectedness of science is to speak of its knowledge as being like the warp and woof of the weaver's cloth. The threads of fact and understanding have only little strength in isolation, but when woven into the fabric of our overall knowledge they gain mutual support from the other threads with which they interact, and thus make up a robust whole.

The concepts by which it is considered appropriate to explain novel phenomena are not so much those that are reduced, simpler or more familiar, but rather those that are integrated into the shared fabric of science or of personal knowledge. A major part of scientific explanation is the recognition of specific new or unexplained phenomena as examples of more general classes of phenomena, for which we already have developed techniques of analysis and prediction. The identification of a rock pattern as a fossil is not actually the explanation of something in terms of its components. Instead it is a process of abstraction; of recognizing a phenomenon as being an example of a general type of thing; of thereby attributing to it a possibly highly complex set of attributes; but attributes which we have already systematized and made part of the matrix of knowledge that we call science. A measurement may be considered the expression of some physical quantity in abstract terms: namely numbers. This is the important sense in which measurement possesses Clarity. But comprehending the semantic content of that abstraction requires prior experience and understanding that is personal and, when pursued to its ultimate roots, eventually non-scientific.

There is another sense of reductionism that is probably more appropriate to apply to science. It is the principle of seeking to describe events in terms of Efficient Causes. Aristotle's science depended upon Final Causes, even for inanimate objects. In modern science the effects follow the causes in accordance with the impersonal, reproducible dictates of natural laws, not because there is any aim in view but because of a specific microscopic causal chain. Seeking Efficient Causes is the modus operandi of science.

When nobel prize-winner Jacques Monod says that "The cornerstone of the scientific method is ... the systematic denial that `true' knowledge can be got at by interpreting phenomena in terms of final causes - that is to say, of `purpose,'"3 he's expressing this principle that science operates by Efficient, not Final, causes. [He's also confusing "true knowledge" with science in a classic example of scientism, but let that pass.] Science rules out explanation in terms of personality, and hence rules out purpose, from the beginning, as an operational postulate. If a cause and its effect are to be truly reproducible, then the cause cannot be a free agent. Free agents' actions are precisely not reproducible. That non-reproducibility we take to be one of the evidences of agency. Purpose in the sense of intentionality of an agent is ruled out of science's descriptions by presumption.

There are aspects of Clarity, also, that exclude agents and purpose. Natural science generally regards introspective observation as lacking sufficient Clarity to be admissible as science. This is one of the most important distinctions between natural philosophy (science) and just plain philosophy. Incidentally, common sense tells us that introspective observation can never be fully excluded from knowledge that involves humans, and this is one of the reasons why the status of psychology as natural science is debatable. Persons are not describable impersonally.

There are, then, strong reasons founded in science's reliance on reproducibility and Clarity why science effectively rules out explanations in terms of purpose. Purpose presupposes an agent, a personality. Persons can't be adequately described within the rubrics of reproducibility and Clarity. They are methodologically excluded. And so is purpose.

References & Credits

3. Jacques Monod. Chance and Necessity. An essay on the natural philosophy of modern biology. Vintage, New York, 1972. Translated by Austryn Wainhouse from the French "Le Hasard et la Necessité", 1970.

About the Author

Ian H. Hutchinson is professor of nuclear science and engineering at the Massachusetts Institute of Technology. His primary research interest is plasma physics and its practical applications. He and his MIT team designed, built and operate the Alcator C-Mod tokamak, an international experimental facility whose magnetically confined plasmas are prototypical of a future fusion reactor. He received his bachelor’s degree in physics from Cambridge University and his doctorate in engineering physics from the Australian National University. He directed the Alcator project from 1987 to 2003 and served as head of MIT’s nuclear science and engineering department from 2003 to 2009. In addition to over 200 journal articles on a variety of plasma phenomena, Hutchinson is widely known for his standard monograph on measuring plasmas:Principles of Plasma Diagnostics. For more, see Hutchinson's book Monopolizing Knowledge.

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